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Sharath   copy
 

Sharath copy

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HI..........HOPE THIS WIL HELP U OUT

HI..........HOPE THIS WIL HELP U OUT

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    Sharath   copy Sharath copy Presentation Transcript

    • TECHNICAL SEMINAR ON FAST VISUAL RETRIEVALUSING ACCELERATED SEQUENCE MATCHING PRESENTED BY UNDER THE GUIDANCE xxxxxxxxx Mr/Mrs.xxxxxxx (1xx08is041) HOD,DEPT. OF XXX XXXXXX
    • ABSTRACT We present an approach to represent, match, and index various types of visual data. Primary goal is to enable effective and computationally efficient searches. an image/video is represented by an ordered list of feature descriptors. Similarities b/w such representations are measured by the approximate sequence matching technique. This unifies visual appearance and the ordering information in a holistic manner.
    • Introduction With the rapid growth in image/video production and distribution industry. Necessary to develop technique which enable users to easily access and organize large volumes of visual data. There are 2 major problems regarded to visual retrieval and its organization i.e., (1) lack of design technique for good visualrepresentation and (2) lack of quantitative metrics which can efficientlymeasure similarities b/w each pair of visual data.
    • Continued… The above problems concerned with the visual retrieval and its organization can be overcomed using following approaches… (1) approximate sequence matching technique(also calledLevenshtein distance) (2)extension to local alignment(smith-watermanalgorithm) (3) indexing with a Vocabulary tree (4)Fast matching algorithm
    • 1.Approximate sequence matchingtechnique  compare two pieces of visual data that represented by ordered features.  Its is formulated by Levenshtein distance ci-cost of an operation  The Levenshtein distance is defined as the “minimal cost of sequence of operations that transforms X into Y”
    • Continued…• Operations restricted in levenshtein distance are as follows (1)insertion δ (ε, a) (2)deletion δ (a, ε) (3)substitution δ (a, b)• Levenshtein distance can be computed using dynamic programming
    • Simple example for approximatestring matching
    • 2.Extension to local alignment Extended to search for local alignments b/w two feature sequences. Derives a score v(xi, yj) between two feature vectors xi and yj. v(xi, yj) is +ve if xi and yj are similar. v(xi, yj) is -ve if xi and yj are not similar. Value v(xi, yj) is considered as substitution score. For insertion operation we assign negative scores denoted as v(xi, ε).
    • Continued… Similarly for deletion operation we assign scores denoted as v(ε, yi). optimal local alignment can be computed using S(i,j)=max{0,S(i-1,j)+v(x, ε),S(i,j-1)+v(ε,yj),S(i-1,j-1)+v(x ,yj)} is called Smith-waterman algorithm compares each descriptor of query to every descriptors in the database.
    • 3.Indexing with a vocabulary tree Vocabulary tree-used for indexing all feature vectors extracted from database. Indexing each of the visual descriptors can be done hierarchically using the concept of k-means hierarchical clustering. Other advanced vocabulary tree used for indexing problems is adaptive vocabulary tree Adaptive vocabulary tree adapts to the addition/deletion of instances from the database. Advantage-tree need not to be rebuilt in case of adaptive vocabulary tree when database undergo slight changes. It enables the retrieval time to grow sub linearly with respect to the no of frames in the database.
    • Continued… Levels of an adaptive vocabulary tree grow sub linearly in terms of frame numbers in the database
    • 4.Fast matching algorithm Tries to filter unnecessary alignments which will not lead to successful matching. Uses visual method called a dot plot. Dot plot puts a dot at (i , j) if descriptor i and descriptor j are similar.
    • Continued… Diagonal link(i,j) is established if dot j is positioned at the bottom-right corner of dot i. Diagonal links represent contiguous matched descriptor pairs. Gap link(i,j) is established if dot j is bottom-right positioned with respect to dot i. Gap link allows the operation of insertions and deletions.
    • CONCLUSION Proposed techniques used for effective and computational efficient visual searches. Representation based on sequence of features. We apply the approximate sequence matching method to measure the similarity based on such a representation. Presented the framework using which it speedups the matching process. Fast retrieval of visuals are ensured. Good representation of visual data. Proposed techniques have been demonstrated for use in several visual retrieval applications.
    • REFERENCES D. A. Adjeroh, M. -C. Lee, and I. King. A distance measure for video sequence similarity matching. M. Bertini, A. D. Bimbo, and W. Nunziati. Video clip matching using MPEG-7 descriptors and edit distance. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. J. Law-To, O. Buisson, V. Gouet-Brunet, and N. Boujemaa. Robust voting algorithm based on labels of behavior for video copy detection.
    • Queries?